Stratification of risk of progression to colectomy in ulcerative colitis via measured and predicted gene expression

Mo, Angela; Nagpal, Sini; Gettler, Kyle; Haritunians, Talin; Giri, Mamta; Haberman, Yael; Karns, Rebekah; Prince, Jarod; Arafat, Dalia; Hsu, Nai Yun; Chuang, Ling-Shiang; Argmann, Carmen; Kasarskis, Andrew; Suárez‐Fariñas, Mayte; Gotman, Nathan; Mengesha, Emebet; Venkateswaran, Suresh; Rufo, Paul A.; Baker, Susan S.; Sauer, Cary G.; Markowitz, James; Pfefferkorn, Marian D.; Rosh, Joel R.; Boyle, Brendan M.; Mack, David R.; Baldassano, Robert N.; Shah, Sapana; Leleiko, Neal S.; Heyman, Melvin B.; Griffiths, Anne M.; Patel, Ashish; Noe, Joshua D.; Thomas, Siep; Aronow, Bruce J.; Walters, Thomas D.; McGovern, Dermot P.; Hyams, Jeffrey S.; Kugathasan, Subra; Cho, Judy H.; Denson, Lee A.; Gibson, Greg

doi:10.1016/j.ajhg.2021.07.013

Cited by 8 publications

(2 citation statements)

References 73 publications

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“…Table 4: Final unique genes of UC using DGESA: Here the genes highlighted with blue have confirmed their existence in state-ofthe-art literature as related with UC. For example, TRAF3 [49], BAG6 [50], CCDC80 [51], CDC42SE2 [52] and HSPA9 [53] are confirmed connection with Inflammatory Bowel Syndrome (IBD) like UC.…”

Section: Methodsmentioning

confidence: 99%

A New Differential Gene Expression Based Simulated Annealing for Solving Gene Selection Problem: A Case Study on Eosinophilic Esophagitis and Few Other Gastro-Intestinal Diseases

Sinha,

Chakraborty,

Bardhan

et al. 2024

Preprint

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Identification of the set of genes collectively causes a disease is an important problem, called gene selection problem. This study introduces two distinct approaches for gene selection in the context of biological diseases: the Ranked Variance (RV) method and Differential Gene Expression Based Simulated Annealing (DGESA). The RV method prioritizes genes based on their variance, offering an initial perspective on potential biomarkers. DGESA, on the other hand, employs simulated annealing, integrating differential gene expression data to refine gene selection further. Through a case study focused on Eosinophilic Esophagus (EoE) and other gastro-intestinal diseases, we compare and contrast the outcomes of both methods. Notably, we identify 10 common genes between RV and DGESA in EoE, highlighting their complementary nature. Validation analyses reveal that 13 out of 40 final genes identified by DGESA for EoE are corroborated by existing literature, indicating their biological relevance. Similarly, in Ulcerative Colitis (UC) and Crohn's Disease (CD), 8 and 7 genes, respectively, out of the final 40 genes identified exhibit confirmation in the literature. These findings underscore the efficacy of both RV and DGESA in elucidating molecular signatures associated with gastro-intestinal diseases, contributing to our understanding of their pathogenesis and potential therapeutic targets.

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Section: Methodsmentioning

confidence: 99%

A New Differential Gene Expression Based Simulated Annealing for Solving Gene Selection Problem: A Case Study on Eosinophilic Esophagitis and Few Other Gastro-Intestinal Diseases

Sinha,

Chakraborty,

Bardhan

et al. 2024

Preprint

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show abstract

“…GSE104687 [ 37 ], RNA-sequencing provided at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE104687 . GSE150961 [ 38 ], RNA-sequencing provided at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE150961 . GSE115828 [ 39 ], RNA-sequencing provided at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115828…”

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confidence: 99%

SGCP: a spectral self-learning method for clustering genes in co-expression networks

Aghaieabiane,

Koutis

2024

BMC Bioinformatics

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Background A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules. Results We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines. Conclusion Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

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Biological, genetic and epigenetic markers in ulcerative colitis

Caliendo,

D'Elia,

Makker

et al. 2023

Advances in Medical Sciences

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Stratification of risk of progression to colectomy in ulcerative colitis via measured and predicted gene expression

Cited by 8 publications

References 73 publications

A New Differential Gene Expression Based Simulated Annealing for Solving Gene Selection Problem: A Case Study on Eosinophilic Esophagitis and Few Other Gastro-Intestinal Diseases

A New Differential Gene Expression Based Simulated Annealing for Solving Gene Selection Problem: A Case Study on Eosinophilic Esophagitis and Few Other Gastro-Intestinal Diseases

SGCP: a spectral self-learning method for clustering genes in co-expression networks

Biological, genetic and epigenetic markers in ulcerative colitis

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